Stochastic Range Estimation Algorithms for Electric Vehicles using Data-Driven Learning Models
This work aims at improving the energy consumption forecast of electric vehicles by enhancing the prediction with a notion of uncertainty. The algorithm itself learns from driver and traffic data in a training set to generate accurate, driver-individual energy consumption forecasts.
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| Hoofdauteur: | |
|---|---|
| Formaat: | Elektronisch Hoofdstuk |
| Taal: | Engels |
| Gepubliceerd in: |
Karlsruhe
KIT Scientific Publishing
2022
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| Reeks: | Karlsruher Schriftenreihe Fahrzeugsystemtechnik
6 |
| Onderwerpen: | |
| Online toegang: | OAPEN Library: download the publication OAPEN Library: description of the publication |
| Tags: |
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| Samenvatting: | This work aims at improving the energy consumption forecast of electric vehicles by enhancing the prediction with a notion of uncertainty. The algorithm itself learns from driver and traffic data in a training set to generate accurate, driver-individual energy consumption forecasts. |
|---|---|
| Fysieke beschrijving: | 1 electronic resource (192 p.) |
| ISBN: | KSP/1000143200 9783731511663 |
| Toegang: | Open Access |